I. Field of the Invention
The present invention relates generally to monitoring and control of manufacturing processes, particularly chemical processes, and more specifically, to neural networks in process control of such processes.
II. Related Art
Quality of products is increasingly important. The control of quality and the reproducibility of quality are the focus of many efforts. For example, in Europe, quality is the focus of the ISO (International Standards Organization, Geneva, Switzerland) 9000 standards. These rigorous standards provide for quality assurance in production, installation, final inspection, and testing. They also provide guidelines for quality assurance between a supplier and customer. These standards are expected to become an effective requirement for participation in the EC (European Community) after the removal of trade barriers in 1992.
The quality of a manufactured product is a combination of all of the properties of the product which affect its usefulness to its user. Process control is the collection of methods used to produce the best possible product properties in a manufacturing process.
Process control is very important in the manufacture of products. Improper process control can result in a product which is totally useless to the user, or in a product which has a lower value to the user. When either of these situations occur, the manufacturer suffers (1) by paying the cost of manufacturing useless products, (2) by losing the opportunity to profitably make a product during that time, and (3) by lost revenue from reduced selling price of poor products. In the final analysis, the effectiveness of the process control used by a manufacturer can determine whether the manufacturer's business survives or fails.
A. Quality and Process Conditions
FIG. 19 shows, in block diagram form, key concepts concerning products made in a manufacturing process. Referring now to FIG. 19, raw materials 1222 are processed under (controlled) process conditions 1906 in a process 1212 to produce a product 1216 having product properties 1904. Examples of raw materials 1222, process conditions 1906, and product properties 1904 are shown in FIG. 19. It should be understood that these are merely examples for purposes of illustration.
Product 1216 is defined by a product property aim value 2006 of its product properties 1904. The product property aim values 2006 of the product properties 1904 are those which the product 1216 needs to have in order for it to be ideal for its intended end use. The objective in running process 1212 is to manufacture products 1216 having product properties 1904 which are exactly at the product property aim value(s) 2006.
The following simple example of a process 1212 is presented merely for purposes of illustration. The example process 1212 is the baking of a cake. Raw materials 1222 (such as flour, milk, baking powder, lemon flavoring, etc.) are processed in a baking process 1212 under (controlled) process conditions 1906. Examples of the (controlled) process conditions 1906 are: mix batter until uniform, bake batter in a pan at a preset oven temperature for a preset time, remove baked cake from pan, and allow removed cake to cool to room temperature.
The product 1216 produced in this example is a cake having desired properties 1904. For example, these desired product properties 1904 can be a cake that is fully cooked but not burned, brown on the outside, yellow on the inside, having a suitable lemon flavoring, etc.
Returning now to the general case, the actual product properties 1904 that are in the product 1216 produced in a process 1212 are determined by the combination of all of the process conditions 1906 of process 1212 and the raw materials 1222 that are utilized. Process conditions 1906 can be, for example, the properties of the raw materials 1222, the speed at which process 1212 runs (also called the production rate of the process 1212), the process conditions 1906 in each step or stage of the process 1212 (such as temperature, pressure, etc.), the duration of each step or stage, and so on.
B. Controlling Process Conditions
FIG. 20 shows a more detailed block diagram of the various aspects of the manufacturing of products 1216 using process 1212. FIGS. 19 and 20 should be referred to in connection with the following description.
To effectively operate process 1212, the process conditions 1906 must be maintained at a regulatory controller set point(s) 1404 so that the product 1216 produced will have the product properties 1904 matching the desired product property aim values 2006. This task can be divided into three parts or aspects for purposes of explanation.
In the first part or aspect, the manufacturer must set (step 2008) initial settings of the regulatory controller set points 1404 in order for the process 1212 to produce a product 1216 having the desired product property aim values 2006. Referring back to the example set forth above, this would be analogous to deciding to set the temperature of the oven to a particular setting before beginning the baking of the cake batter.
The second step or aspect involves measurement and adjustment of the process 1212. Specifically, process conditions 1906 must be measured to produce process condition measurements 1224, 1226. The measured process conditions 1906 must be used to generate adjustments a controllable process state(s) 2002 so as to hold the process conditions 1906 as close as possible to regulatory controller set point 1404. Referring again to the example above, this is analogous to the way the oven measures the temperature and turns the heating element on or off so as to maintain the temperature of the oven at the desired temperature value.
The third stage or aspect involves holding the measurements of the product properties 1904 as close as possible to the product property aim values 2006. This involves producing product property measurements 1304 based on the product properties 1904 of the product 1216. From these measurements, adjustments 1402 to a process condition set point(s) 1404 must be made so as to change the regulatory controller set point 1404. Referring again to the example above, this would be analogous to measuring how well the cake is baked. This could be done, for example, by sticking a toothpick into the cake and adjusting the temperature at a predetermined baking time so that the toothpick eventually comes out clean.
It should be understood that the previous description is intended only to show the general conditions of process control and the problems associated with it in terms of producing products of predetermined quality and properties. It can be readily understood that there are many variations and combinations of tasks that are encountered in a given process situation. Often, process control problems can be very complex.
In recent years, there has been a great push towards the automation of process control. The motivation for this is that such automation results in the manufacture of products of desired product properties where the manufacturing process that is used is too complex, too time-consuming, or both, for people to deal with manually.
Thus, the process control task can be generalized as being made up of five basic steps or stages as follows:
(1) the initial setting of process condition setpoint step 2008; PA1 (2) producing process condition measurements 1224, 1226 of the process conditions 1906.; PA1 (3) adjusting to controllable process states 2002 in response to the process condition measurements 1224, 1226; PA1 (4) producing product property measurements 1304 based on product properties 1904 of the manufactured product 1216; and PA1 (5) adjustment to process condition set point 1402 in response to the product property measurements 1304.
The explanation which follows explains the problems associated with meeting and optimizing these five steps.
C. The Control Problem
The step 1208 makes adjustments to the process in response to what is known (measured) about the process or the product.
Using classical process control techniques, the adjustments 1208 to controllable process states 2002 to achieve process condition aim values 1404 or product property aim values 2006 are typically implemented using simple single-input single-output control relationships. However, there are some control problems which cannot be solved with such relationships. One example is visual inspection of the process or product. Human beings can easily perform such inspections. Based on their inspection, they can make adjustments in the process. However, automating this requires using a visual image as input, which does not work for a single input control method. Visual images can be generated and captured automatically, but the result is a three-dimensional (two spatial dimensions plus an intensity dimension) analog signal, or the equivalent discretized and digitized matrix of intensity. Moreover, a human may ascertain more than one result from a visual inspection. Multiple outputs further complicate the use of classical control methods.
In this situation, a human operator can properly perform the control task using visual image as input and producing changes to controllable process states to achieve control objectives. Since the proper responses can be generated for a sequence of input signals, it would be desirable if this could be automated.
In some manufacturing processes, a human operator may be able to make adjustments 1208 to one or more controllable process states 2002 in response to one or more product property measurements 1304 or process condition measurements 1224. Because human operators often do not have extensive theoretical training in the underlying physical principles of the manufacturing process, it may be impossible for them to describe the methods they use to make these adjustments. Their methods may actually implement a relationship which uses product property measurements 1304 and process condition measurements 1224 as inputs, and creates adjustments 1402 to process condition setpoint values 1404 as outputs. They may also use property aim values 2006 or process condition aims 1404 as input. This relationship could possibly be reproduced if it was known. However, when human operators cannot describe this relationship it is impossible to automate this using classical control methods.
In this situation, the ultimate conformance of product properties 1904 to product property aim values 2006 and of process conditions 1906 to process condition aim values 1404 is totally dependent upon the behavior of the human operators controlling the process. Since the methods used by the human operators may not be readily explainable, the performance in controlling product properties 1904 from one operator to another may vary widely. Moreover, since the methods used by the best operators are not readily described, it may be difficult to transfer these techniques from one operator to another. Thus, a method of reproducing the control methods of the best operators would again be very helpful.
D. Deficiencies of Conventional Controllers for Adjusting Controllable Process States
As stated above, the adjustments 1208 of controllable process states 2002 can sometimes only be carried out by human operators using a number of product property measurements 1304 and process condition measurements 1224. Because human operators may not be able to define the relationships between the measurements 1304, 1224 and the adjustments 1208, the performance of these human operators is difficult, if not impossible, to perform effectively using classical controllers.
In a classical controller, the relationship between adjustment 1208 and measurement 1304, 1224 is typically defined by an algorithm which implements a single-input single-output relationship. That is, the algorithm uses a single measurement 1304, 1224 as input, and produces a single adjustment 1208 as output. Obviously such single-input single-output control relationships would be inadequate to reproduce human operators' behavior which used multiple measurements as input.
Classical controllers are also algorithmic. That is, they use a fixed algorithm or equation to relate the adjustment 1208 to the measurement 1304, 1224. This algorithm is a simple mathematical equation. It may use the current value of the measurement, the derivative of the measurement, the integral of the error (that is the difference between the measurement and the aim value 2006 or setpoint value 1404), or it may use other aspects of the measurement. However, in all cases the control relationship between the measurement and the adjustment is defined beforehand by an equation. These types of controllers are difficult, if not impossible, to apply in situations where the relationship between the adjustment and the measurement is not well understood.
Thus, it can be seen that conventional controllers are very difficult to apply in situations where process adjustments 1208 are properly carried out by people but not based on explicitly known algorithmic relationships.
E. Deficiencies of Expert Systems for Adjusting Controllable Process States
Classical control techniques require a well defined mathematical control relationship between the inputs and the outputs. This control relationship must be well known before the control using classical techniques can be implemented.
Expert systems are a different approach to control which are not based on predefined algorithmic functions. They can be beneficial in automating process control under certain circumstances. Expert systems are essentially decision-making programs which base their decisions on process knowledge, which is typically represented in the form of if-then rules. Each rule in an expert system makes a small statement of truth, relating something that is known about the process to something that can be inferred from that knowledge. By combining the applicable rules, an expert system can reach conclusions or make decisions which mimic the decision-making of human experts.
However, like algorithmic controls, expert systems require a complete understanding of the nature of the process knowledge to be automated before the expert system can be implemented. Thus, it can be seen that expert system based control methods also have significant limitations when applied to problems where the adjustment methodology can be performed by an operator but is not well understood.